Obstacle-Avoidable Robotic Motion Planning Framework Based on Deep Reinforcement Learning

doi:10.1109/TMECH.2024.3377002

Obstacle-Avoidable Robotic Motion Planning Framework Based on Deep Reinforcement Learning

2024-12-01

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DOI：10.1109/TMECH.2024.3377002

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Although robotic trajectory generation has been extensively studied, the motion planning in environments with obstacles still faces some open issues and is yet to be explored. In this article, a universal motion planning framework based on deep reinforcement learning (DRL) is proposed to achieve autonomous obstacle avoidance for robotic tasks. First, a prophet-guided actor-critic structure based on the expert strategy is designed, which can realize prompt replanning when the task scenario changes. Second, an expansive dual-memory sampling mechanism is proposed to efficiently augment expert data from only a few demonstrations. It also improves the training efficiency of DRL algorithms through an increasingly unbiased sampling rule. Third, a composite obstacle-avoidable reward system is designed to achieve collision-free motion for both a robot's end effector and its body/link. It can build a dense reward map, and strike a balance between obstacle avoidance and action exploration. Finally, experimental results have validated the performance of the proposed work in three different scenes.

Keywords:

Robots

Task analysis

Planning

Collision avoidance

Trajectory

Training

Picture archiving and communication systems

Composite obstacle-avoidable reward (COR)

deep reinforcement learning (DRL)

expansive dual-memory sampling (EDS)

prophet-guided actor-critic (PAC)

robotic motion planning

Obstacle-Avoidable Robotic Motion Planning Framework Based on Deep Reinforcement Learning

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